Computer vision systems have been implemented in the past few years for applications such as face, finger print, iris, target recognition and general object recognition. These applications involve limited domains of interest such as face recognition under controlled conditions at airports. “Face in a crowd” applications in wide spaces or corridors have not been accurate enough to replace human operators in multiple camera surveillance systems. Humans and primates still outperform the best computer vision systems by almost any measure. False alarm rates and false rejection rates are usually too high to make the systems viable.
How the brain recognizes objects is now being studied world wide in earnest. A system that emulates human object recognition seems to be a “Holy Grail”. Indeed, Dharmendra Modha, Director of Cognitive Computing at IBM, stated “We have no computers today that can begin to approach the awesome power of the human brain. A computer comparable to the human brain, he added, would need to be able to perform more than 38 thousand trillion operations per second.” See Mhoda, Dharmendra, IEEE 125th Anniversary Celebration Speech, New Yorker Hotel, Oct. 3, 2009.
In parallel with these efforts, the field of neuromorphic computing has emerged from a concept developed by Carver Mead in the late 1980's. The term neuromorphic has to do with the combining of analog and digital techniques in such a way as to emulate the brain's circuitry and architecture.
Other Information
Chirp Fourier Transform.
A chirp Fourier transform (CFT) is in reality a Fourier transform. The chirp Fourier transform lends itself more readily to an analog implementation than a straight Fourier transform. The methodology of the chirp Fourier transform is as follows: 1) Multiply the input spectrum by a chirped waveform, 2) Convolve the chirped input spectrum with the impulse response function of the matched dispersive delay line, and 3) Multiply the convolved result by the inverse of the input chirp multiplier. In an analog version steps 1 and 3 are accomplished with mixers and step 2 is accomplished by utilizing a physical dispersive delay line (DDL). A compressive receiver is a special case of the CFT in that the bands of interest are wider than the bandwidth of the DDL. The down side of this condition is that there are gaps in the signals of interest such that only narrowband signals can be reconstructed completely. More information about these approaches is in two issued U.S. Pat. Nos. 4,649,392 entitled “Two dimensional transform utilizing ultrasonic dispersive delay line” and 4,646,099 entitled “Three dimensional Fourier transform device”.
Fast Pattern Recognizer.
It is also known that a fast pattern recognizer (FPR) can be based on reduced dimensionality frequency domain convolution techniques which is a general pattern recognizer capable of operating on any 2-D pattern with non-zero gradient information. It performs well on degraded (e.g. blurred, smudged, or partially obscured) inputs, allowing reasonable operation using imperfect enrollment or sensor data. The operation requiring the most processing power is the matched filter/correlation stage. Such FPR algorithms have been implemented completely in software on for example a standard laptop, and also on a desk top computer with the correlator being digitally emulated by an FPGA. An analog/digital mixed-mode PCI based expansion board for use in desktops used a surface acoustic wave (SAW) dispersive delay line (DDL) to implement a Chirp Fourier Transform (CFT) convolver. This technology is further described in detail in U.S. Pat. No. 5,859,930. FPR has been used for speech recognition and speaker identification in the presence of noise or other speakers and in law enforcement where police composites were processed by the FPR to find the perpetrators in police mug shot data bases.